Data mining algorithms : explained using R
Material type:
- 9781118332580
- 006.312 CIC/D
Includes bibliographical references and index.
Part I: Preliminaries
Covers learning tasks (classification, regression, clustering), basic statistics, visualization, and practical issues.
Part II: Classification
Discusses decision trees, Naïve Bayes, linear classifiers, misclassification costs, and model evaluation.
Part III: Regression
Explores linear regression, regression trees, and performance evaluation, with extensions beyond linearity.
Part IV: Clustering
Focuses on similarity measures, k-means, hierarchical clustering, and quality evaluation metrics.
Part V: Enhancing Models
Includes ensemble methods, kernel techniques (SVMs), attribute transformation, discretization, and selection.
Case Studies & Appendices
Real-world applications (e.g., Census data, crime analysis), R packages, datasets, and notations.
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